Researchers presented the RETD algorithm for stabilizing reinforcement learning

Researchers have published a paper describing Regularized Emphatic Temporal-Difference Learning (RETD) — a new method aimed at addressing the instability issues when using constant learning steps in reinforcement algorithms. The standard Emphatic TD (ETD) approach often encounters the problem of positive Lyapunov exponents when sampling data, leading to divergence of the process. The authors of the study proposed a normalized first-order correction mechanism that preserves weights and importance ratios, using scalar state leakage to store the signal and subsequent delayed correction. In experiments involving 10 000 runs, the RETD algorithm demonstrated certified negative indices on two-component constructs, where classical ETD showed instability. The method allows recovering the fixed point of ETD using one- and two-regularization readings, ensuring convergence with harmonically decreasing steps. The proposed approach does not reduce trace variance but significantly changes the dynamics after system shock states. The results confirm that RETD effectively copes with task-specific dependency, providing more predictable model behavior in off-policy learning conditions. The work contributes to the theory of reinforcement learning, offering a mathematically grounded solution for stabilizing agent training in complex environments. The original data and theorem proofs are available under the CC BY-NC-ND 4.0 license.